Cystic formation in human primary brain tumors is a relatively rare event whose incidence varies widely according to the histotype of the tumor. Composition of the cystic fluid has mostly been characterized in samples collected at the time of tumor resection and no indications of the evolution of cystic content are available. We characterized the evolution of the proteome of cystic fluid using a bottom-up proteomic approach on sequential samples obtained from secretory meningioma (SM), cystic schwannoma (CS) and cystic high-grade glioma (CG). We identified 1008 different proteins; 74 of these proteins were found at least once in the cystic fluid of all tumors. The most abundant proteins common to all tumors studied derived from plasma, with the exception of prostaglandin D2 synthase, which is a marker of cerebrospinal fluid origin. Overall, the protein composition of cystic fluid obtained at different times from the same tumor remained stable. After the identification of differentially expressed proteins (DEPs) and the protein–protein interaction network analysis, we identified the presence of tumor-specific pathways that may help to characterize tumor–host interactions. Our results suggest that plasma proteins leaking from local blood–brain barrier disruption are important contributors to cyst fluid formation, but cerebrospinal fluid (CSF) and the tumor itself also contribute to the cystic fluid proteome and, in some cases, as with immunoglobulin G, shows tumor-specific variations that cannot be simply explained by differences in vessel permeability or blood contamination.

Glioblastoma is a highly aggressive, infiltrative brain tumor of the central nervous system (CNS). Its extensive molecular and biochemical heterogenicity hinders the identification of reliable biomarkers and therapeutic targets, thereby making prognosis and existing therapy ineffective. In recent years, breakthroughs in the use of proteomics on a range of biological samples, such as plasma, cerebrospinal fluid (CSF), tissues, brain cells, and exosomes, represent a potential improvement to GBM investigations. Mass spectrometry-based approaches represent an important technique in the characterization of the tumoral proteome, for the identification of differentially expressed proteins, and for studying altered molecular pathways involved in tumor stages. Proteomics studies advance our knowledge about GBM pathogenesis, the discovery of reliable diagnostic and prognostic markers, and therapeutic approaches, also. In this context, for the effective application of proteomics on GBM, it is mandatory to develop a translational network by integrating hospitals, biobanks, and research institutions into a single network, to enable a collaborative approach across disciplines, thereby enabling rapid translation to clinical application of new proteomic insights. Today, high-quality biobanks play a key role in enabling collaborative, ethically compliant research, supporting the effective application of proteomics in glioblastoma studies and the translation of discoveries into clinical practice. This review explores current trends in proteomics and GBM research, highlighting how leveraging biobank infrastructure and fostering institutional cooperation can drive the development of targeted pilot projects to enhance the impact and effectiveness of glioblastoma research.

Targeting fibrosis in Duchenne muscular dystrophy (DMD)-associated cardiomyopathy is a critical outstanding clinical issue, as cardiac failure remains a leading cause of death despite advances in supportive care. This study evaluates the therapeutic efficacy of CSPG4-targeted chimeric antigen receptor (CAR) T cells in reducing cardiac fibrosis and improving heart function in a preclinical model of the disease. DMD is a progressive genetic disorder characterized by degeneration of skeletal and cardiac muscle. Cardiomyopathy, driven by fibrosis and chronic inflammation, is a leading contributor to mortality in affected patients. Proteoglycans such as CSPG4, critical regulators of extracellular matrix dynamics, are markedly overexpressed in dystrophic hearts and promote pathological remodeling. Current treatments do not adequately target the fibrotic and inflammatory processes underlying cardiac dysfunction. CSPG4-specific CAR-T cells were engineered and administered to dystrophic mice. Therapeutic efficacy was assessed through histological, molecular, and echocardiographic analyses evaluating cardiac fibrosis, inflammation, innervation, and overall function. Treatment with CSPG4 CAR-T cells preserved myocardial integrity, improved cardiac performance, and reduced both fibrosis and inflammatory markers. The therapy also restored cardiac innervation, indicating a reversal of neural remodeling commonly seen in muscular dystrophy-related cardiomyopathy. CSPG4-targeted CAR-T therapy offers a novel, cell-based strategy to mitigate cardiac remodeling in dystrophic hearts. By addressing core fibrotic and inflammatory drivers of disease, this approach represents a significant advancement in the development of precision immune therapies for muscular dystrophies and cardiovascular conditions.

Multicenter studies are essential for benchmarking analytical workflows, yet their interpretation is often confounded by the combined effects of experimental protocols and instrumentation. To address this challenge, we introduce a simple normalization-based analytical framework, the recovery metric (ρ), designed to decouple protocol driven effects from instrument dependent variability. We applied this framework to the 13th Proteomics Multicentric Experiment (PME13), a large multicentric proteomics dataset generated across 27 laboratories using high sensitivity workflows and varying sample preparation protocols. By leveraging a common digested reference sample, ρ enables direct cross-comparison of all datasets on a unified scale, effectively minimizing instrument-related biases. Using this approach, we demonstrate that apparent instrument dependent trends are largely removed when evaluated through ρ, revealing consistent protocol driven effects across laboratories. Statistical modeling identified key variables influencing ρ, including sample input amount, reduction and alkylation, and the use of n-dodecyl-β-D-maltoside (DDM). While DDM was associated with improved ρ, reduction and alkylation and additional handling steps led to reduced performance, particularly at low input levels. We further highlight practical considerations for the application of ratio based normalization, including the occurrence of values exceeding theoretical bounds, which reflect deviations from underlying assumptions and require appropriate filtering. Overall, this work establishes a generalizable analytical strategy for disentangling confounding factors in multicentric datasets and provides practical guidelines for optimizing high sensitivity proteomics (HSP) workflows. The proposed framework is broadly applicable to other analytical fields where cross laboratory comparability is required.

Obesity is a major global health challenge characterized by chronic low-grade inflammation, oxidative stress, and an increased risk of cardiometabolic disorders. Although sex-related differences in inflammatory and redox biomarkers have been reported in obese populations, the molecular mechanisms underlying this heterogeneity remain incompletely understood. In this study, we applied a proteomics-based approach to investigate urinary extracellular vesicles from 45 obese individuals (BMI 30–40 kg/m²; age 50–70 years) in order to identify molecular signatures associated with metabolic dysregulation. Shotgun proteomics analysis performed by nanoLC–MS/MS enabled the identification of 3822 proteins. Hierarchical clustering of proteomic profiles revealed two distinct molecular groups, predominantly enriched in males (Group I) and females (Group II). Label-free quantitative analysis identified 466 differentially abundant proteins between the two clusters. Functional enrichment analysis highlighted pathways associated with immune response, metabolic regulation, and redox homeostasis, including glycolysis/gluconeogenesis, lysosome activity, leukocyte transendothelial migration, and glutathione, cysteine and methionine metabolism. Notably, proteins related to ferroptosis were enriched, suggesting the involvement of iron-dependent oxidative cell death mechanisms in the metabolic imbalance observed in a subset of subjects. Furthermore, the non-enzymatic glycosylation of urinary proteins was significantly higher in Group I compared with Group II (p = 0.0002), indicating increased formation of advanced glycation products in individuals with a more pronounced pro-oxidant state. Preliminary follow-up data suggested a higher incidence of pathological events, including cardiovascular complications, among individuals belonging to Group I. Overall, these findings demonstrate that urinary proteomic profiling can identify distinct molecular phenotypes among obese individuals and highlight oxidative stress, ferroptosis, and protein glycation as potential determinants of metabolic vulnerability, supporting the use of non-invasive proteomic approaches for improved risk stratification in obesity.

Immunoglobulin light chains (AL) and transthyretin (ATTR) characterize most cardiac amyloidosis cases. Several proteomic studies have analyzed microdissected amyloid plaques. However, they did not capture the molecular dysfunctions triggered in surrounding tissue. We, therefore, holistically investigated the protein profiles of endomyocardial biopsies from patients with ALλ and ATTR conditions. Both conditions showed extracellular matrix remodeling and downregulation of proteostasis, signaling, and mitochondrial energetics. The results also suggested vesicle-mediated transport and complement activation as hallmarks of patients with ALλ and ATTR conditions, respectively. These findings corroborate previously proposed pathological mechanisms, namely, direct mitochondrial toxicity of light chain fibrils internalized via macropinocytosis and massive extracellular deposition of ATTR. Notably, protein quantification and network topology enabled the identification of potential disease markers. Although the selection criteria were robust, validation in larger cohorts is needed. Nevertheless, given the challenging accessibility of the analyzed samples, the proposed landscape of results provides a valuable resource to encourage further studies on cardiac amyloidosis.